Working Memory Load Modulates Neuronal Coupling

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1–3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC–FEF–LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load

Intrinsic neuronal dynamics predict distinct functional roles during working memory

Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF), and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We find that cells with short timescales carry memory information relatively early during memory encoding in lPFC; whereas long-timescale cells play a greater role later during processing, dominating coding in the delay period. We also observe a link between functional connectivity at rest and the intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predict complex neuronal dynamics during WM, ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.Biotechnology and Biological Sciences Research Council (Great Britain) (BB/M010732/1)United States. Office of Naval Research (N00014-14-1-0681)National Institute of Mental Health (U.S.) (R00MH092715)National Institute of Mental Health (U.S.) (R37MH087027)Massachusetts Institute of Technology. Picower Innovation FundUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (grant N00014-16-1-2832)National Institute for Health Research (Great Britain). Wellcome Trust (203139/Z/16/Z

Gamma and Beta Bursts Underlie Working Memory

Working memory is thought to result from sustained neuron spiking. However, computational models suggest complex dynamics with discrete oscillatory bursts. We analyzed local field potential (LFP) and spiking from the prefrontal cortex (PFC) of monkeys performing a working memory task. There were brief bursts of narrow-band gamma oscillations (45–100 Hz), varied in time and frequency, accompanying encoding and re-activation of sensory information. They appeared at a minority of recording sites associated with spiking reflecting the to-be-remembered items. Beta oscillations (20–35 Hz) also occurred in brief, variable bursts but reflected a default state interrupted by encoding and decoding. Only activity of neurons reflecting encoding/decoding correlated with changes in gamma burst rate. Thus, gamma bursts could gate access to, and prevent sensory interference with, working memory. This supports the hypothesis that working memory is manifested by discrete oscillatory dynamics and spiking, not sustained activity.National Institute of Mental Health (U.S.) (Grant 5R01MH091174-05)National Institute of Mental Health (U.S.) (Grant 5R37MH087027-07

Materiality in information environments: Objects, spaces, and bodies in three outpatient hemodialysis facilities

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152032/1/asi24277.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152032/2/asi24277_am.pd

Epithelial-to-mesenchymal transition drives a pro-metastatic Golgi compaction process through scaffolding protein PAQR11

Tumor cells gain metastatic capacity through a Golgi phosphoprotein 3-dependent (GOLPH3-dependent) Golgi membrane dispersal process that drives the budding and transport of secretory vesicles. Whether Golgi dispersal underlies the prometastatic vesicular trafficking that is associated with epithelial-to-mesenchymal transition (EMT) remains unclear. Here, we have shown that, rather than causing Golgi dispersal, EMT led to the formation of compact Golgi organelles with improved ribbon linking and cisternal stacking. Ectopic expression of the EMT-activating transcription factor ZEB1 stimulated Golgi compaction and relieved microRNA-mediated repression of the Golgi scaffolding protein PAQR11. Depletion of PAQR11 dispersed Golgi organelles and impaired anterograde vesicle transport to the plasma membrane as well as retrograde vesicle tethering to the Golgi. The N-terminal scaffolding domain of PAQR11 was associated with key regulators of Golgi compaction and vesicle transport in pull-down assays and was required to reconstitute Golgi compaction in PAQR11-deficient tumor cells. Finally, high PAQR11 levels were correlated with EMT and shorter survival in human cancers, and PAQR11 was found to be essential for tumor cell migration and metastasis in EMT-driven lung adenocarcinoma models. We conclude that EMT initiates a PAQR11-mediated Golgi compaction process that drives metastasis

Pre-Stimulus Activity Predicts the Winner of Top-Down vs. Bottom-Up Attentional Selection

Our ability to process visual information is fundamentally limited. This leads to competition between sensory information that is relevant for top-down goals and sensory information that is perceptually salient, but task-irrelevant. The aim of the present study was to identify, from EEG recordings, pre-stimulus and pre-saccadic neural activity that could predict whether top-down or bottom-up processes would win the competition for attention on a trial-by-trial basis. We employed a visual search paradigm in which a lateralized low contrast target appeared alone, or with a low (i.e., non-salient) or high contrast (i.e., salient) distractor. Trials with a salient distractor were of primary interest due to the strong competition between top-down knowledge and bottom-up attentional capture. Our results demonstrated that 1) in the 1-sec pre-stimulus interval, frontal alpha (8–12 Hz) activity was higher on trials where the salient distractor captured attention and the first saccade (bottom-up win); and 2) there was a transient pre-saccadic increase in posterior-parietal alpha (7–8 Hz) activity on trials where the first saccade went to the target (top-down win). We propose that the high frontal alpha reflects a disengagement of attentional control whereas the transient posterior alpha time-locked to the saccade indicates sensory inhibition of the salient distractor and suppression of bottom-up oculomotor capture

Free choice activates a decision circuit between frontal and parietal cortex

We often face alternatives that we are free to choose between. Planning movements to select an alternative involves several areas in frontal and parietal cortex that are anatomically connected into long-range circuits. These areas must coordinate their activity to select a common movement goal, but how neural circuits make decisions remains poorly understood. Here we simultaneously record from the dorsal premotor area (PMd) in frontal cortex and the parietal reach region (PRR) in parietal cortex to investigate neural circuit mechanisms for decision making. We find that correlations in spike and local field potential (LFP) activity between these areas are greater when monkeys are freely making choices than when they are following instructions. We propose that a decision circuit featuring a sub-population of cells in frontal and parietal cortex may exchange information to coordinate activity between these areas. Cells participating in this decision circuit may influence movement choices by providing a common bias to the selection of movement goals